{"url":"/dataset/eqasc","name":"eQASC","full_name":"eQASC","description_markdown":"This dataset contains 98k 2-hop explanations for questions in the QASC dataset, with annotations indicating if they are valid (~25k) or invalid (~73k) explanations.\r\n\r\nThis repository addresses the current lack of training data for distinguish valid multihop explanations from invalid, by providing three new datasets. The main one, eQASC, contains 98k explanation annotations for the multihop question answering dataset [QASC](https://allenai.org/data/qasc), and is the first that annotates multiple candidate explanations for each answer.\r\n\r\nThe second dataset, eQASC-perturbed, is constructed by crowd-sourcing perturbations (while preserving their validity) of a subset of explanations in QASC, to test consistency and generalization of explanation prediction models. The third dataset eOBQA is constructed by adding explanation annotations to the [OBQA dataset](https://allenai.org/data/open-book-qa) to test generalization of models trained on eQASC.\r\n\r\nSource: [Allen Institute for AI](https://allenai.org/data/eqasc)","description_withheld":null,"homepage":"https://allenai.org/data/eqasc","introduced_date":"2020-10-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/learning-to-explain-datasets-and-models-for","title":"Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering","first_author":"Harsh Jhamtani","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Reasoning Chain Explanations","url":"/task/reasoning-chain-explanations","datasets_with_task":"/datasets/task/reasoning-chain-explanations"}],"languages":[],"variants":["eQASC"],"data_loaders":[],"num_papers_in_archive":13,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}